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About This Role
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Humana is looking for an AI Engineering Principal to serve as a senior technical leader within the Simplification Center of Excellence (CoE), driving the architecture, technology strategy, and delivery of enterprise\-scale simplification capabilities. This role partners with senior executives and cross\-functional teams to design secure, scalable solutions that enable workflow understanding, rapid prototyping, and high\-impact service improvements across the organization. The Principal Engineer will translate complex business needs into clear technical roadmaps, establish engineering standards, and guide platform and integration decisions, while mentoring teams and fostering best practices. This leader ensures that simplification initiatives are built on durable, adaptable architectures that accelerate innovation, improve experiences, and support long\-term enterprise success.
Program Summary
The Simplification Center of Excellence (CoE) is building a new enterprise capability that helps teams create simpler, more effective, and more human‑centered solutions for associates, members, patients, and providers. Joining this team means contributing directly to how the organization understands work today and builds better experiences for the future.
The CoE develops shared tools, methods, and guidance that support teams from discovery through delivery—bringing together process insight, research, ideation, rapid prototyping, and impact assessment to improve both speed and quality of outcomes. By establishing these capabilities, the team enables more informed decisions, stronger solution design, and greater confidence in what gets built and delivered.
This work involves designing and building the foundational systems that enable these capabilities—translating complex workflows and insights into scalable, reliable, and well‑architected solutions. Engineers on the team work closely with product, design, and research partners to prototype, test, and harden new approaches, while helping define technical standards, data flows, and extensible patterns that can be reused across the enterprise.
Position Overview
We are seeking an AI Engineering Principal to serve as a senior technical leader within the Simplification Center of Excellence (CoE), owning the technical architecture, technology strategy, and implementation of the CoE's core simplification and acceleration capabilities. In this role, you will act as a trusted technical advisor to senior executives, helping shape and drive enterprise‑wide service simplification initiatives that improve how work is designed, delivered, and experienced across the organization.
As a Principal Engineer, you will lead the design and evolution of scalable, secure, and extensible systems that enable discovery, workflow understanding, rapid prototyping, and solution validation. You will partner closely with product, experience, data, and engineering teams to translate complex business and operational needs into clear technical architectures, design patterns, and implementation roadmaps. Your work will ensure that simplification capabilities are reliable, and able to support high‑impact use cases across multiple teams and domains.
In this role, you will collaborate directly with senior leaders to align technical strategy with enterprise priorities, providing guidance on tradeoffs, sequencing, and long‑term sustainability. You will set technical standards for the CoE, influence platform and integration decisions, and help establish best practices for building and scaling shared capabilities across the enterprise.
As a senior technical leader and mentor, you will provide hands‑on leadership and coaching to engineers on the team, fostering strong engineering practices, thoughtful design, and high‑quality delivery. Through your leadership, you will help raise the technical bar of the organization while enabling faster, clearer execution—ensuring that simplification initiatives are grounded in sound architecture and built to endure.
Key Responsibilities
- Advise and make recommendations to senior IT and business leaders on areas for service simplification.
- Define and maintain system architecture and technology stack.
- Collaborate with product managers, designers, business intelligence engineers, and other stakeholders to make technical decisions, define technical requirements, and create development roadmaps in service of business goals.
- Conduct code reviews, provide constructive feedback, and promote continuous improvement in code quality and development processes.
- Mentor engineers and enforce best practices.
- Contribute hands\-on across the stack.
- Collaborate with DevOps for CI/CD and cloud deployments.
- Participate in hiring and onboarding new team members.
Core Skills \& Attributes
- Full\-stack expertise (React, TypeScript, .NET/Java, Python).
- Cloud architecture (Azure/GCP), Kubernetes, Postgres.
- AI architecture (LLMs, Vector DBs, embeddings, prompting, agents).
- Test\-driven development across unit, functional, integration, and UI testing.
- Systems thinking skillset.
- Passionate about continuously improving and simplifying customer experiences through human\-centered design.
Use your skills to make an impact
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Qualifications \& Experience
- 10\+ years software engineering experience; 3\+ years in architecture/lead roles.
- Strong leadership, communication, and collaboration skills in a cross\-functional product team. 2 or more years of experience leading people or projects.
- Demonstrated ability to work across product, engineering, operations, and delivery teams to align needs, opportunities, and solutions.
- Experience supporting or accelerating product development processes
Preferred Qualifications
- Enterprise security/compliance knowledge
- DevOps knowledge such as containerization and infrastructure as code
- Data engineering knowledge/experience such as ETL and data pipelines
- Human\-centered design
- Interest or desire for pair\-programming
- Understanding of training and deploying machine learning models
Additional Information
Qualified candidates are required to currently live in, or be willing to move to, a commutable distance for a hybrid (\~3 days in\-office) work arrangement. Locations include:
- Washington, D.C. metropolitan area
- Dallas, TX metropolitan area
- Louisville, KY metropolitan area
- New York City, NY metropolitan area
- Chicago, IL metropolitan area
Work at Home Criteria
To ensure Home or Hybrid Home/Office employees' ability to work effectively, the self\-provided internet service of Home or Hybrid Home/Office employees must meet the following criteria:
- We recommend at minimum a download speed of 25 Mbps and an upload speed of 10 Mbps; we suggest a wireless, wired cable or DSL connection.
- Satellite, cellular and microwave connection leadership approves only if used.
- We will provide employees who live and work from Home in the state of California, Illinois, Montana, or South Dakota a bi\-weekly payment for their internet expense.
- Humana will provide Home or Hybrid Home/Office employees with telephone equipment appropriate to meet the our requirements for their position/job.
- Work from a dedicated space lacking ongoing interruptions to protect member PHI / HIPAA information.
Interview Format
As part of our hiring process for this opportunity, we will use an interviewing technology called HireVue to enhance our hiring. Modern Hire allows us to quickly connect and gain valuable information from you about your relevant experience at a time that is best for your schedule.
Benefits
Humana offers a variety of benefits to promote the best health and well\-being of our employees and their families. We design competitive and flexible packages to give our employees a sense of financial security—both today and in the future, including:
- Health benefits effective day 1
- Paid time off, holidays, volunteer time and jury duty pay
- Recognition pay
- 401(k) retirement savings plan with employer match
- Tuition assistance
- Scholarships for eligible dependents
- Parental and caregiver leave
- Employee charity matching program
- Network Resource Groups (NRGs)
- Career development opportunities
SSN Alert
Humana values personal identity protection. Please be aware that applicants may be asked to provide their Social Security Number, if it is not already on file. When required, an email will be sent from [email protected] with instructions on how to add the information into your official application on Humana’s secure website.
Scheduled Weekly Hours
40Pay Range
The compensation range below reflects a good faith estimate of starting base pay for full time (40 hours per week) employment at the time of posting. The pay range may be higher or lower based on geographic location and individual pay will vary based on demonstrated job related skills, knowledge, experience, education, certifications, etc.
$206,600 \- $284,300 per year
This job is eligible for a bonus incentive plan. This incentive opportunity is based upon company and/or individual performance.Description of Benefits
Humana, Inc. and its affiliated subsidiaries (collectively, “Humana”) offers competitive benefits that support whole\-person well\-being. Associate benefits are designed to encourage personal wellness and smart healthcare decisions for you and your family while also knowing your life extends outside of work. Among our benefits, Humana provides medical, dental and vision benefits, 401(k) retirement savings plan, time off (including paid time off, company and personal holidays, volunteer time off, paid parental and caregiver leave), short\-term and long\-term disability, life insurance and many other opportunities.About Us
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About Humana: Humana Inc. (NYSE: HUM) is a leading U.S. healthcare company. Through our Humana insurance services and our CenterWell healthcare services, we make it easier for the millions of people we serve to achieve their best health – delivering the care and service they need, when they need it. These efforts are leading to a better quality of life for people with Medicare and Medicaid, families, individuals, military service personnel, and communities at large. Learn more about what we offer at Humana.com and at CenterWell.com.
Equal Opportunity Employer
It is the policy of Humana not to discriminate against any employee or applicant for employment because of race, color, religion, sex, sexual orientation, gender identity, national origin, age, marital status, genetic information, disability or protected veteran status. It is also the policy of Humana to take affirmative action, in compliance with Section 503 of the Rehabilitation Act and VEVRAA, to employ and to advance in employment individuals with disability or protected veteran status, and to base all employment decisions only on valid job requirements. This policy shall apply to all employment actions, including but not limited to recruitment, hiring, upgrading, promotion, transfer, demotion, layoff, recall, termination, rates of pay or other forms of compensation and selection for training, including apprenticeship, at all levels of employment.
Salary Context
This $206K-$284K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% of the market. At Humana, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($245K) sits 35% above the category median. Disclosed range: $206K to $284K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Humana AI Hiring
Humana has 7 open AI roles right now. They're hiring across Data Scientist, AI/ML Engineer, AI Software Engineer, AI Product Manager. Positions span Remote, US, New York, NY, US, Fort Lauderdale, FL, US. Compensation range: $173K - $284K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (112) are outnumbered by mid-level (1,798) and senior (1,516) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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